Analyzing of flexible gripper by computational intelligence approach

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@Article{Petkovic:2016:Mechatronics,
  author =       "Dalibor Petkovic and Srdjan Jovic and Obrad Anicic and 
                 Bogdan Nedic and Branko Pejovic",
  title =        "Analyzing of flexible gripper by computational
                 intelligence approach",
  journal =      "Mechatronics",
  volume =       "40",
  pages =        "1--16",
  year =         "2016",
  ISSN =         "0957-4158",
  DOI =          "doi:10.1016/j.mechatronics.2016.09.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957415816300940",
  abstract =     "Adaptive grippers should be able to detect and
                 recognize grasping objects. To be able to do it control
                 algorithm need to be established to control gripper
                 tasks. Compliant underactuated mechanisms with passive
                 behavior can be used for modelling of adaptive robotic
                 fingers. Undearactuation is a feature which allows
                 fully adaptability of robotic fingers for different
                 objects. In this study gripper with two fingers was
                 established. Finite element method (FEM) procedure was
                 used to optimize the gripper structural topology.
                 Kinetostatic model of the underactuated finger
                 mechanism was analyzed. This design of the gripper has
                 embedded sensors as part of its structure. The use of
                 embedded sensors in a robot gripper gives the control
                 system the ability to control input displacement of the
                 gripper and to recognize specific shapes of the
                 grasping objects. Since the conventional control
                 strategy is a very challenging task, soft computing
                 based controllers are considered as potential
                 candidates for such an application. The sensors could
                 be used for grasping shape detection. Given that the
                 contact forces of the finger depend on contact position
                 of the finger and object, it is suitable to make a
                 prediction model for the contact forces in function of
                 contact positions of the finger and grasping objects.
                 The prediction of the contact forces was established by
                 using a soft computing (computational intelligence)
                 approach. To perform the contact forces estimation
                 adaptive neuro-fuzzy (ANFIS) methodology was used. FEM
                 simulations were performed in order to acquire
                 experimental data for ANFIS training. The main goal was
                 to apply ANFIS network in order to find correlation
                 between sensors' stresses and finger contact forces.
                 Afterwards ANFIS results were compared with benchmark
                 models (extreme learning machine (ELM), extreme
                 learning machine with discrete wavelet algorithm
                 (ELM-WAVELET), support vector machines (SVM), support
                 vector machines with discrete wavelet algorithm
                 (SVM-WAVELET), genetic programming (GP) and artificial
                 neural network (ANN)). The reliability of these
                 computational models was analyzed based on simulation
                 results.",
  keywords =     "genetic algorithms, genetic programming, Compliant
                 gripper, Adaptive gripper, Underactuation, Contact
                 forces, ANFIS",
}

Genetic Programming entries for Dalibor Petkovic Srdan Jovic Obrad Anicic Bogdan Nedic Branko Pejovic

Citations